Talk about vaccines and vaccination is everywhere, especially in terms of the equitability of the vaccination rollout. A major concern is that those communities that have been hit hardest by the vaccine—Black and Latinx Americans, many of whom bear the scars of racially-driven medical neglect and experimentation—are least excited to get it. BARI and our colleagues at UMass Boston’s Center for Survey Research and the Boston Public Health Commission conducted a survey of Bostonians regarding their experience and attitudes during the pandemic, including an item about intentions to be vaccinated. Like others, we found racial disparities in who did and didn’t plan to receive the vaccine. But we wanted to go beyond these simple descriptions by to do something no one else has done: quantify their implications for community outcomes using a simulation model.
Our simulation model was similar to those used by public health experts and network scientists that models the proportion of individuals in a community who are susceptible to infection, currently infected, and recovered over time (i.e., an SIR model). It also took into consideration movement between communities in approximating the exposure of susceptible individuals and their subsequent likelihood of infection. We ran this model for October-December 2020, using historical infection rates and mobility data drawn from cell phone records, allowing us to make a clear comparison with what would have happened without vaccination. This model contains a lot of math and assumptions, of course, so we encourage those interested in those details to read the full report.
Figure 1. Evolution of infections over the course of the three-month simulation without vaccination (gold line) and with vaccination (blue line), broken out by ethnic composition of the community.
As we described in yesterday’s data story, the three-month simulation saw stark disparities in the uptake of vaccination across communities, with some communities of color seeing as little as 59% of its population receive the vaccine. The impacts of these disparities were evident in the corresponding evolution of infection rates. As shown in Figure 1, infection rates across communities kept pace with the no-vaccination scenario until mid-October, which is when vaccination began to substantially lower the population susceptible to infection. The growth curve for infection rates under vaccination had about the same shape across communities of different racial composition, with all communities seeing substantial drops in infections relative to no vaccination. However, infections were still three times greater in high Black-Latinx communities than predominantly White communities (0.09 infections vs. 0.27 infections per 1,000 residents). These disparities were apparent when mapped across communities in Figure 6, largely matching the distribution of vaccination.
Figure 2. Estimated infectionrates across municipalities in the region (left panel) and ZIP codes in Boston (right panel) at the end of the simulation.
Moving beyond infection rates, did communities reach the goal of herd immunity via vaccination? We defined herd immunity as the point at which a community had 0 infections (as opposed to the proxy of what percentage of people have immunity, in which case an entrenched virus could still cause infections). We found that only 27% of communities had reached herd immunity by this definition at the end of the simulation. We thus extended the simulation for three additional months. An additional 52% of communities achieved herd immunity in the fourth month of the extended simulation (80% cumulative), and all but one remaining community achieved herd immunity in the fifth month, which reached it shortly thereafter. The average community reached herd immunity on day 103 of the simulation.
Figure 3. The month at which each municipality in the region (left panel) and each ZIP code in Boston (right panel) reached herd immunity in the simulation.
Last, it is important to note that a simulation is only as strong as its assumptions, and we have been forced to make many. To evaluate the robustness of our conclusions, we tested a variety of alterations to our model. These are described in detail in the report, but the most crucial lessons came from edits to the level of persuasion, especially because outreach is the focus of many in the public health community right now. We found that a substantial increase to the rate of persuasion only had marginal benefits across communities. Those with a high Black-Latinx population saw herd immunity only 10 days earlier than in the baseline model, while predominantly White communities saw an even smaller improvement of 2 days. This closed the gap between the two groups by less than 20%. Yet, if we eliminated persuasion altogether, we say the doomsday scenario of predominantly White communities reaching herd immunity and high Black-Latinx communities never doing so—even when given the full six months.
The failure of a stronger persuasion rate to substantially improve matters indicates that the proportion of people committed to being vaccinated at the outset and, conversely, the proportion committed to not being vaccinated, are highly consequential in determining the timeline of reaching herd immunity. What does this mean for moving forward? First, public messaging and outreach should be immediately amplified as much as possible in order to increase the percentage of those willing to receive the vaccine once it is their turn.
Second, this messaging and outreach must reject the assumption that those who say they are “definitely not” going to get the vaccine cannot be swayed. Otherwise, we resign ourselves to the fact that 1 in 5 Black and Latinx residents in greater Boston would never be vaccinated. This would inevitably hinder the elimination of the virus. Even though we see widespread herd immunity in the model, in terms of communities having zero infections, this does not guarantee that infections cannot return. The middling rates of vaccination that appear possible for communities of color could still produce gaps in the protective wall of community health.
The content of this post is drawn from the Living in Boston during COVID-19 survey conducted by the Center for Survey Research at UMass Boston and the Boston Area Research Initiative at Northeastern University, in collaboration with the Boston Public Health Commission. It was funded by the National Science Foundation’s Human-Environment and Geographical Sciences (HEGS) program through a grant for rapid-response research (RAPID; Award #2032384). The results presented here were part of a longer report on “The Inequitable Consequences of Vaccination Intentions.”
